Unknown object grasping using statistical pressure models

Abstract

Grasping is one of the most fundamental and challenging tasks in robotics. Applications range from space missions (e.g., collection of rock samples) to industrial automation. In this work, we use a camera mounted on the end-effector of a manipulator to grasp an unknown object in the workspace. A novel deformable contour model is used to determine plausible grasp axes of the target object. Potential grasp point pairs are generated, ranked based upon measurements taken from the contour, and a vision-guided grasp of the object using the highest ranked grasp point pair is executed. Several experimental results are presented.